This session will focus on various ET estimation methods, including sap flow or soil heat pulse sensors, lysimeters, eddy covariance stations, scintillometers, and remote sensing. We will also explore new techniques like AI, data fusion, sharpening algorithms, machine learning, and cloud computing. Additionally, we will cover detailed evaluations of scale dependencies, strategies to handle uncertainties, systematic biases, and the representativity of estimates.
We welcome contributions that (1) assess and compare various in-situ and remote sensing methods, (2) analyse trends and spatio-temporal patterns in ET data, including error sources and uncertainty, (3) bridge scales between different in-situ measurements, modelled and remotely sensed ET, including validation and calibration challenges, (4) evaluate challenges and opportunities of applying AI methods, cloud computing and new technologies.
We plan to have a little networking meetup at the session posters in the lunch break, so please feel free to join, bring your lunch and have a chat. :)
Over a century of study of ecosystem water fluxes has resulted an abundance of in-situ measurement techniques causing the availability of robust and continuous measurements to quietly grown by orders of magnitude in the last few years. For example, the ten years since the release of the FLUXNET 2015 synthesis dataset (which contained records dating back 25 years) has more than doubled the amount of eddy covariance measurements publicly released, with now over a million total days of measurements taken from over 450 sites globally. Furthermore, other dataset synthesis efforts for sap flux, soil moisture, stream flow, etc., as well as combinations with proximal and remote sensing, quickly result in datasets much larger than can be tackled by an individual. The advancement of machine learning and computational power to digest and utilize this deluge of environmental data hold promise to be able to understanding global water cycles in an unprecedented detail. However, limitations to applying machine learning methods often comes not from computational power, but rather in understanding the particular uncertainties and nuances, as well as unique information on ecosystem functioning, that each dataset brings.
Here, I briefly outline the current state of the art of scaling ecosystem water fluxes from in-situ to regional and global scales through the example of eddy covariance and the FLUXCOM-X framework [1]. Particularly, I highlight the current sources of uncertainties, such as measurement corrections and spatial extrapolation, as well as the potential limitations of machine learning and artificial intelligence in tackling these issues. Furthermore, comparing up-scaled eddy covariance evapotranspiration and transpiration products to terrestrial land surface models demonstrates the discrepancy in the global ratio of transpiration to ET between process based and data driven methods, demonstrating how machine learning from in-situ scales can inform our understanding of global cycle. Finally, I explore how integration of multiple data sources holds promise in isolating individual ecosystem water fluxes and to link the local measurements of individual plants and ecosystems to the regional and global scales.
1 - Nelson and Walther et al., 2024. X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X. Biogeosciences 21, 5079–5115. https://doi.org/10.5194/bg-21-5079-2024
How to cite: Nelson, J. A.: Scaling terrestrial ecosystem water fluxes at the interface of in-situ measurements and machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15919, https://doi.org/10.5194/egusphere-egu25-15919, 2025.
Forests modulate precipitation and evapotranspiration fluxes. One important – yet often overlooked – component in the forest water cycle is the forest-floor litter layer. Organic matter on the forest floor retains significant amounts of annual precipitation (i.e., throughfall), subsequent evaporation from these forest-floor litter layers enhances below-canopy humidity, thereby potentially reducing atmospheric water demand in closed canopy stands. Evaporation fluxes from the forest floor are often attributed to transpiration, because partitioning of evaporation and transpiration is difficult and thus typically has large uncertainties. Here, we hypothesize that current partitioning estimates that do not account for forest-floor evaporation overestimate forest transpiration rates.
Previous measurements at our “WaldLab Forest experimental site” in Zurich and additional litter sampling in ~400 plots across the European Alps showed that needle and broadleaf litter retained up to 18% of annual precipitation or on third of annual evapotranspiration (ET), leading to substantial overestimates of recharge and transpiration in Alpine forest ecosystems. Here, we compare these results with temporally high-resolved water vapor flux data measured above- and below-canopy at the Swiss FluxNet sites Lägeren (CH-Lae; mixed deciduous forest) and Davos (CH-Dav; evergreen coniferous forest). We estimated the potential contribution of litter-layer evaporation to total below-canopy ET, by calculating half-life storage decay in the litter layer. The maximum water retention capacity of the forest-floor litter layer was estimated from soil moisture measurements at 5 cm depth, and the litter-layer retention timescales were estimated from changes in below-canopy ET after precipitation events. Overall, we found that roughly 60% of below-canopy ET at the Lägeren and Davos sites can be attributed to litter-layer evaporation, thereby suggesting overestimation of transpiration in water balance estimates and potential underestimation of tree water use efficiency.
How to cite: Floriancic, M. G., Hörtnagl, L., Krebs, L., Scapucci, L., Feigenwinter, I., Shekhar, A., and Buchmann, N.: Estimating forest-floor litter evaporation from above- and below-canopy flux tower data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18865, https://doi.org/10.5194/egusphere-egu25-18865, 2025.
Spatial evaporation estimates are essential information for studying the water cycle, yet the amount of direct observations, such as Eddy-Covariance (EC) networks, are limited. Satellites can also provide spatial evaporation estimates, but these are based on indirect measurements of surface conditions and contain many assumptions. As a new method, we explore the potential of commercial microwave links (CMLs), such as used in cellular telecommunication networks, to be used as scintillometers. Scintillometers are dedicated instruments to measure path-integrated latent and sensible heat fluxes, which transmit electromagnetic radiation that is diffracted by turbulent eddies between transmitter and receiver, the so-called scintillation effect. CMLs are also line-of-sight devices that transmit electromagnetic radiation at similar frequencies as microwave scintillometers. Here, we estimate 30-min latent heat fluxes and daily evaporation estimates using the received signal level from a CML sampled at 20 Hz. To do so, we use data of a 38 GHz Nokia Flexihopper CML (formerly part of a telecom network) installed over an 856 m path at the Ruisdael Observatory near Cabauw, the Netherlands. We compare our results with estimates of a combined optical and microwave scintillometer setup, as well as an EC system.
Before obtaining flux estimates, we correct for the white noise present in the signal of the CML, based on power spectra of the CML and the microwave scintillometer, and obtain 30-min estimates of the structure parameter of the refractive index Cnn. Subsequently, to obtain the flux estimates from these Cnn estimates, we apply the two-wavelength method, in combination with the optical scintillometer, as well as a standalone energy-balance method (EBM), requiring net radiation estimates. Also, we consider the free-convection scaling of Monin-Obukhov similarity theory (MOST), instead of the complete scaling. An advantage of this scaling is that it removes the need for horizontal wind speed measurements, which are more difficult to obtain in complex environments. For the net radiation estimates, we use in-situ measured radiation and data products provided by the Satellite Application Facility on Land Surface Analysis (LSA SAF) of EUMETSAT.
Considering both turbulent heat fluxes, the two-wavelength method outperforms the EBM. The standalone EBM shows a reasonable performance, but depends heavily on the quality of the net radiation estimates. When aggregating our 30-min latent heat fluxes to daily evaporation estimates, the overall performance for both methods remains comparable. These daily evaporation estimates could also be useful for hydrological applications, e.g., for catchment-scale water budget studies. Moreover, application of the free-convection scaling instead of the complete MOST scaling results in a comparable performance for all methods. Before adoption of our methods to obtain evaporation estimates using CML networks, the influence of different CML design types and their sampling strategies in operational networks on the obtained flux estimates needs to be studied. If these are successfully addressed, CMLs could show a large potential to estimate evaporation, especially considering that existing CML networks are present at locations where evaporation observations are lacking.
How to cite: van der Valk, L., Hartogensis, O., Coenders-Gerrits, M., Hut, R., Walraven, B., and Uijlenhoet, R.: Can we estimate evaporation using commercial microwave links?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4159, https://doi.org/10.5194/egusphere-egu25-4159, 2025.
Understanding the water cycle is increasingly crucial to assess ecosystem resilience and ensure sustainable management and food security. Within the terrestrial water cycle, Evapotranspiration (ET) plays a pivotal role returning 60% of terrestrial precipitation back to the atmosphere. In agricultural systems, especially in water-scarce regions, understanding the water use of crops relative to their productivity (water use efficiency, WUE) is of paramount importance.
The AgroFlux sensor platform, including an automatic, robotic FluxCrane, is part of a long-term experiment in an agricultural system. We combine three years of ET measurements and two years of fully automated water stable isotope measurements coupled with campaign-based soil and plant measurements. The system is measuring along an erosion gradient with three different soil types to examine small scale heterogeneity of soils and their effect during various environmental conditions on different crops. The automated system generates data with high temporal and spatial resolution resulting in a new class of data that both enables and demands modern, efficient data analysis approaches. We use data-driven machine learning modeling approaches as an interface between the high-resolution monitoring networks and campaign-based measurements to provide better predictive results.
With our research we try to improve the knowledge of evapotranspiration by using novel modeling approaches coupled with measurements of common environmental parameters, plant specific parameters and water stable isotopes. We are investigating the potential of evapotranspiration estimation and modeling, and the possibility of automatically measuring and modeling the isotopic signature of evapotranspiration to decompose the water cycle into its components.
How to cite: Dahlmann, A., Dubbert, D., Schmidt, M., Verch, G., Marshall, J. D., Augustin, J., Hoffmann, M., and Dubbert, M.: Disentangling water flux dynamics on an eroded cropland using an automated chamber system, water stable isotopes, and novel data-driven machine learning approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18042, https://doi.org/10.5194/egusphere-egu25-18042, 2025.
Transpiration flux estimations for individual trees usually rely on point-information obtained from a limited number of sap flow sensors and wood core samples. The underlying assumption is that sap flux densities and wood properties are sufficiently homogeneous within one tree and well represented by a few sensor measurements and wood samples. If this assumption is justified or not, however, has rarely been experimentally tested and quantitatively evaluated. Our objective was to quantify the variability of individual sap flux measurements within one tree and to answer the question how the observed uncertainty could most effectively be reduced.
We installed 23 sap flow sensors into, and took 30 wood core samples from one specimen of Pinus sylvestris. Eventually, we obtained six stem cross sections from the same tree. This extensive sampling allowed us to asses the within-tree variability of sap flow velocities, wood densities and sapwood depths. Based on our various measurements, we applied a boot-strapping scheme to quantify the uncertainty of tree level transpiration flux estimates that would result from different numbers of installed sap flow sensors and extracted wood cores.
Our results indicate that the temporal courses of sap flux densities within our studied tree were highly correlated to each other (R² >= 0.98), but their absolute values varied considerably (coefficient of variation (CV) of 11.3% and 26.6% for outer and inner measurement depths, respectively) without showing a remarkable spatial pattern. Wood densities were the least variable parameter (CV of 2.5%), while the uncertainty of the conducting sapwood area varied across six stem cross sections (CVs between 8% and 14%).
We conclude, that the within-tree variability of sap flux densities and sapwood areas – even for a tree stem without any remarkable anomalies – can quickly lead to considerable errors of sap flux estimates. In our case, the heterogeneity of sap flux densities (especially within the inner sapwood) was so high, that it dominated the overall uncertainty. Consequently, the most effective way to reduce the uncertainty of our sap flux estimates was to increase the number of installed sap flow sensors, while additional wood core information only started to pay off in conjunction with higher numbers (≥4) of installed sap flow sensors. A reduction of the overall sap flux uncertainty (CV of 16 % for one sap flow sensor and one wood core) to a CV around 5% would have required at least seven sap flow sensors combined with information of eight wood cores, but could as well have been achieved with ten sap flow sensors combined with the information of two wood cores.
How to cite: Seeger, S. and Maier, M.: How many sap flow sensors and wood cores are required to accurately measure the sap flux of one tree?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6242, https://doi.org/10.5194/egusphere-egu25-6242, 2025.
In terrestrial ecosystems, forest stands are the primary drivers of atmospheric moisture and local climate regulation, making the quantification of transpiration (T) at the stand level both highly relevant and scientifically important. Stand-level T quantification complements evapotranspiration monitoring by eddy-covariance systems, providing valuable insight into the water use efficiency of forested ecosystems in addition to serving as important inputs for the calibration and validation of global transpiration monitoring products based on satellite observations.
Stand level T estimates are typically obtained by scaling up individual tree estimates of water movement within the xylem – or sap flow. This movement affects the radius of a tree stem, whose fluctuations over the diel cycle provide pertinent information about tree water relations which can be readily detected by point (or precision) dendrometers. While sap flow measurements have greatly advanced our understanding of water consumption (T) at the level of individual trees, deploying conventional sap flow monitoring equipment to quantify T at the level of entire forested stands (or ecosystems) can quickly become costly since sap flow measurements from many trees are required to reduce the uncertainty of the upscaling.
Using a boreal old-growth Norway spruce stand at an ICOS site in Southern Norway as a case study, we assess the potential of augmenting conventional sap flow monitoring systems with sap flow modeling informed by point dendrometer measurements to reduce the uncertainty of stand level T estimation at the daily resolution. We test the hypothesis that the uncertainty reduction afforded by a boosted tree sample size more than offsets the propagation of uncertainty originating from the point dendrometer-based sap flow estimates.
How to cite: Bright, R., Creek, D., Lange, H., Meissner, H., Merlin, M., and Zhao, J.: Point dendrometers are simple and reliable tools for improving forest transpiration estimation accuracy at stand scales , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8862, https://doi.org/10.5194/egusphere-egu25-8862, 2025.
Global evapotranspiration (ET) products are critical for modeling climate, hydrology, land surface processes, and managing water resources. These products are derived using diverse methodologies, including machine learning, energy balance, and process-based models. While many studies have assessed ET products, they typically focus on specific regions or basins. Moreover, no intercomparison has specifically addressed irrigated areas, despite their significant role in regional climate and hydrology. To fill this gap, this study evaluates eight global ET products (FLUXCOM, MOD16A2, ERA5-Land, GLDAS-Noah, GLEAMv4, MERRA2, SSEBOP, PML v2) across 12 irrigated regions in the contiguous United States, Spain, Italy, Australia, China and India, characterized by diverse irrigation practices, climates, and crop types. The analysis examines ET dynamics and magnitudes in relation to auxiliary irrigation data (timing, equipment rates, and climate), includes a spatial evaluation of ET against the Global Map of Irrigated Areas (GMIA), and analyzes the spatial patterns of the ET/ETP ratio. The products are also locally validated using in situ ET measurements from five Eddy Covariance towers located in irrigated fields in California and Italy. Our results reveal substantial discrepancies among ET products in their ability to: i) detect irrigation signals, ii) capture seasonal irrigation patterns, and iii) estimate ET volumes consistent with crop water needs and local climatic conditions. Furthermore, the relationship between ET dynamics and irrigation information differs significantly between regions, sometimes even for the same product. These findings highlight the need to enhance global ET products to better incorporate irrigation dynamics, improving their utility for water management, climate modeling, and assessments of anthropogenic impacts on the Earth system.
How to cite: Laluet, P., Corbari, C., and Dorigo, W.: Intercomparison of global evapotranspiration products over irrigated areas using irrigation auxiliary information and in situ Eddy Covariance tower measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17630, https://doi.org/10.5194/egusphere-egu25-17630, 2025.
In recent years, the Alpine region has experienced an increasing frequency of drought events, leading to periods of reduced water availability and consequent impacts on both agricultural and hydropower production. Evapotranspiration (ET) is a key variable for detecting drought conditions and optimizing water resource management, yet accurate estimates of ET at high spatial resolution remain scarce in mountainous regions. Remote sensing has become a valuable tool for generating spatially distributed ET maps using thermal infrared data. Among the existing methods, the Two-Source Energy Balance (TSEB) model has demonstrated robust performance across diverse land types and climates. In this work, we run TSEB simulations forced by input data optimized for complex terrain to assess the model's behavior in the Alpine region. Key datasets include topographically corrected high-resolution solar irradiance derived from a radiation product based on Meteosat Second Generation data (0.05° spatial resolution) and a high-resolution (5-m) land-cover map specific to the Alpine region. Vegetation height was obtained from a 30-m canopy height map derived from the Global Ecosystem Dynamics Investigation (GEDI) dataset, while biophysical parameters were estimated using distinct algorithms for forested and non-forested areas. We present a validation of the TSEB model at eddy covariance (EC) sites distributed across the Alpine region, representing a wide range of elevations and diverse land cover types. The model's performance was assessed using four configurations: (1) observed input variables from EC sites, (2) the standard Sen-ET implementation of TSEB using coarse resolution data, (3) high-resolution inputs as described above, and (4) a configuration incorporating meteorological data from a high-resolution analysis dataset. This work contributes to the PNRR project RETURN (Multi-risk science for resilient communities under a changing climate) and to the Italian National Drought Hydrological Monitoring System (NatDHMS).
How to cite: Deidda, P., Bartkowiak, P., and Castelli, M.: Improving Two-Source Energy Balance Modeling of Evapotranspiration in Complex Terrain: Validation at Alpine Eddy Covariance Sites, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13491, https://doi.org/10.5194/egusphere-egu25-13491, 2025.
Although Evapotranspiration (ET) has long been recognised as a key process for the redistribution of water and energy at a global scale, there remains uncertainty in the actual ET rates at a local scale from vegetated Sustainable Drainage Systems (SuDS). ET has been seen to account for between 61 % and 21 % of the water balance in these systems, demonstrating its significance in the overall system performance. There is a requirement to improve our understanding of the variance of ET rates from SuDS and similar systems. By extension, there is a need for robust ET estimation methods which can be readily applied to a variety of SuDS at different spatial and temporal scales.
The Three-Temperatures (3-T) method is one such approach, which only requires net radiation and surface temperatures from the vegetated surface and a corresponding imitation surface, alongside the overlying air temperature. This method has been previously applied to a variety of different surface types, spatial scales and environments. However, it has been met with a varying degree of success and often only produced spot ET estimates. Furthermore, its limitations are not fully understood and producing a continuous record of ET estimates allows us to see when and under what conditions spot estimates of 3-T ET may be considered credible.
This preliminary study aimed to determine if reasonable continuous ET estimates could be achieved from the 3-T method for a small vegetated surface analogous to SuDS and or green infrastructure (GI). This included the establishment of an experimental setup, which captured the relevant 3-T parameters and those required to calculate hourly reference ET rates as determined by the FAO 56 Penman–Monteith (P-M) method, to use for comparison purposes. Practical considerations (e.g. building shadowing) and sensitivity analysis of 3-T ET estimates to changes in the 3-T parameters were also explored, to provide a deeper understanding of the method’s robustness.
Initial results indicated that the 3-T method can produce periods of ‘reasonable’ continuous hourly ET values, between 0.0 mm.hr-1 to 0.5 mm.hr-1 under preferred conditions. Following a period (up to 3 days) of dry weather conditions, the cumulative reference ET was 2.3 mm and the corresponding 3-T ET was 2.9 mm, showing a total difference of 26% at the end of 3 days. The tendency of the 3-T method to produce higher ET estimates during the day compared to the reference ET values, was attributed to instances where the surface temperatures approach that of the air temperature. The preliminary findings show promise for the 3-T method to produce continuous records of ET, but have also highlighted the need for further research on the method’s application to vegetated SuDS and or GI.
How to cite: Wickham, B., Stovin, V., and De-Ville, S.: A preliminary study on the feasibility of continuously estimating evapotranspiration from vegetated surfaces using the three-temperatures method., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13187, https://doi.org/10.5194/egusphere-egu25-13187, 2025.
Woody crops such as almond and pistachio orchards are proliferating very fast in arid and semi-arid agricultural regions. This is the case of the southeastern Spanish region of Castilla-La Mancha, where the shortage of water resources and the low rainfall during the crop growing season under these conditions, makes it necessary to conduct efficient use of irrigation water in order to improve the sustainability of these crops.
A variety of Remote Sensing based (RS-based) surface energy balance (SEB) models have been shown effective to estimate crop evapotranspiration (ETc), and capture water stress conditions, using satellite imagery. Although their performance sometimes depends on the crop type or the environmental conditions. In addition, some limitations remain for an operational and continuous monitoring of daily ETc at a fine spatial and temporal resolution for water management or irrigation scheduling purposes, particularly on nut orchards. A model ensemble might help in overtaking these shortcomings.
Recent efforts in the framework of the WATERSNUTS project (“remote sensing and digital farming for sustainable water use in almond and pistachio orchards”) have combined computational design with well-stablished SEB approaches into a Python environment to generate daily maps of distributed ETc covering Castilla-La Mancha region, for a selected time period and a predefined spatial resolution, starting with 20 m x 20 m. Up to now, two models, the Mapping Evapotranspiration with Internalized Calibration (METRIC) and Two-Source Energy Balance (TSEB), were implemented for testing, and a time series of Landsat 8-9, and Sentinel 2-3 were used as inputs. Whereas METRIC stands on VNIR and TIR data from Landsat series at 30-m pixel size, the implemented version of TSEB adopts a disaggregated Land Surface Temperature (LST) at 20-m spatial resolution, that has already shown good results in previous research applied to the tandem Sentinel-2 (S2)/Sentinel-3 (S3).
The reference evapotranspiration, ETo, plays a key role in this computational framework to fill the daily gaps with no available satellite images. A layer of 5-km gridded observational daily ETo values was provided by the Spanish State Meteorological Agency (AEMET). A self-derived crop classification map was used to focus the analysis on the nut orchards and discern between irrigated and rainfed plots, and look into the differences between water regimens.
Before upscaling, a local assessment was conducted in an agricultural area located in Tarazona de La Mancha, Spain (39º 15’ 58’’ N, 1º 56’ 23” W), for the period 2021-2024, using data from two full-equipped eddy-covariance towers installed at the center of an almond and a close by pistachio orchards.
The ensemble results are promising for nut orchards such as almonds or pistachio plantations, since S3-S2 disaggregated LST can help in increasing the frequency of daily ETc estimates through TSEB modeling in reduced size plots, while METRIC can outperform for those days with Landsat overpass. Further integration of additional SEB approaches, or RS-based water balance estimates, would enrich the ensemble, and foster the constrain of the uncertainty in evapotranspiration monitoring in nut orchards.
How to cite: Sánchez, J. M., Moya, A., Nieto, H., Sánchez-Virosta, Á., Galve, J. M., and González-Piqueras, J.: Towards an ensemble of RS-based SEB models to constrain the uncertainty in daily ETc monitoring in nut orchards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18779, https://doi.org/10.5194/egusphere-egu25-18779, 2025.
Accurate estimation of evapotranspiration (ET) in drylands is critically dependent on capturing fine-scale spatial variability, yet current thermal remote sensing approaches face significant scaling limitations. While satellite-based thermal imagery provides broad coverage for ET estimation, its coarse resolution fails to capture the heterogeneous vegetation patterns characteristic of dryland ecosystems, leading to systematic biases in ET estimates. The non-linear relationship between land surface temperature (LST) and ET means that coarse-resolution LST measurements cannot simply be averaged to estimate ecosystem-scale ET. Instead, the underlying spatial variance in LST must be properly accounted for when scaling between observations at different resolutions. Here, we demonstrate an approach using very high resolution (VHR) UAV-derived thermal imagery (0.3-m resolution) combined with multi-scale satellite observations (up to 90-m resolution) to develop scaling relationships between LST variance and spatial resolution. We show how these relationships vary with vegetation composition and seasonal dynamics in a dryland ecosystem over one year. By modeling how LST variance changes across scales, we can better estimate ET from coarser thermal imagery while preserving the influence of fine-scale heterogeneity. Our results indicate that vegetation pattern and phenological stage significantly influence scaling behavior, allowing us to identify optimal measurement resolutions for different ecosystem conditions. This approach reduces uncertainty in ET estimates from satellite thermal imagery by incorporating the effects of sub-pixel spatial variability revealed by VHR observations. The scaling relationships we develop provide a framework for improving regional ET estimates in drylands while accounting for their characteristic fine-scale vegetation patterns.
How to cite: Caylor, K., Amin, S., Morgan, B., and Trugman, A.: Capturing Fine-Scale Variability in Dryland Evapotranspiration Through Multi-Scale Thermal Image Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16088, https://doi.org/10.5194/egusphere-egu25-16088, 2025.
Actual evapotranspiration (ET) is commonly the largest extractive term in the land surface water balance, thus representing a key component of any water management activity and water resource quantification. Unfortunately, in-situ ET observations are often expensive, sporadically collected, and representative only of local conditions. In this context, modelling approaches represent a widespread alternative for the characterization of ET over large areas and for log time periods. While most of the spatially-distribute ET estimation approaches relies on satellite data to some extent, not all these estimates can be considered as satellite ET products. Like other satellite-based datasets, ET estimates are indirect in nature, and often depend on modelling approaches characterized by a variety of approaches and input requirements integrating a mixture of satellite and non-satellite datasets. With continuous advancements and developments in satellite data, the number of continental to global satellite ET products are increasing and they are characterized by a vast variety of sensors and modelling methods. This increasing number of available ET products underscores the need for a concerted effort in defining the standards and protocols for validation and evaluation exercises, which is the main goal of the Committee on Earth Observation Satellite (CEOS) Land Product Validation (LPV) subgroup. In this research, an overview of the methodologies adopted for the assessment of satellite-based ET will be provided, with a focus on the key hypotheses and forcings representing the “satellite” component of the approaches. This overview will provide a common reference of what constitute a satellite-based ET product, to be investigated by the CEOS LPV subgroup in the definition of recommended protocols to assess the accuracy and reliability of current and future continental to global satellite ET datasets.
How to cite: Cammalleri, C. and the CEOS Land Product Validation Subgroup - Evapotranspiration: An overview of satellite-based evapotranspiration products in the framework of the CEOS Land Product Validation Subgroup, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8943, https://doi.org/10.5194/egusphere-egu25-8943, 2025.
The resilience of regional hydrology in human-influenced landscapes is a key challenge in the context of climate change. Lusatia in East Germany is an example of a region facing complex challenges in water management due to massive open pit mining activities as well as being subject to increasing water climate-induced scarcity. This study presents a comprehensive validation and comparative analysis of multi-temporal satellite-based evapotranspiration evapotranspiration (ET) data at multiple spatial resolutions including the 2000m Central Europe Refined Analysis (CERv2) – a product derived from the Weather Research and Forecasting (WRF) model forced by ERA5 reanalysis – alongside the 500m Moderate Resolution Imaging Spectroradiometer (MODIS) global product and 30m Landsat based ET, using lysimeter and eddy covariance measurements as ground-based references. This approach aims to assess the accuracy and practical utility of these data products for informing regional water management strategies. For the first time, a long-term analysis of landscape water balance changes and resilience is conducted, focusing on evapotranspiration as a central parameter for assessing the spatial and temporal variability of water dynamics. To compare the time series data, metrics such as Mean Absolute Error (MAE) were used to evaluate the agreement between satellite-based datasets and reference measurements. Our results reveal differences in the absolute values of evapotranspiration across the datasets. MODIS data, for instance, tend to underestimate evapotranspiration in water-saturated areas, while Landsat data appear to overestimate evapotranspiration in forested areas. These findings suggest the presence of systematic deviations influenced by specific hydrological conditions and land use types. Despite these differences, the datasets exhibit strong consistency in terms of spatial patterns as well as of generic temporal dynamics, suggesting that the key processes driving evapotranspiration are reliably represented. Analysis of long-term ET trends highlights the sensitivity of different land use types to climatic changes. Notably, all datasets indicate an increasingly earlier seasonal decline in ET on agricultural land over the past 20 to 30 years, reflecting shifts in water availability patterns. These findings provide a foundation for advancing water management models and developing sustainable management concepts. The insights not only support local management strategies but can also offer transferable frameworks for addressing similar challenges in comparable landscapes in Central Europe.
How to cite: Kröcher, J., Ghazaryan, G., and Lischeid, G.: Monitoring Changes in the Landscape Water Balance: A Comparative Analysis of Satellite-Based Evapotranspiration Data in the Northern German Lowlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11213, https://doi.org/10.5194/egusphere-egu25-11213, 2025.
Land surface temperature (LST) derived from satellite thermal sensors is a crucial dataset for environmental and urban studies. However, the limited spatial resolution and accuracy of these datasets may present significant challenges for various applications. This study introduces two innovative approaches to improve the spatial resolution and accuracy of LST: (1) a regression-based model integrating multiple sources of information and (2) a physically-based model of the Surface Energy Balance (SEB). The regression-based model employs, a decision-level fusion approach to minimize the impact of various error sources. Regression approaches include various combinations of regression models and different training and implementation strategies. In this study, four models were employed to develop an appropriate relationship between LST and environmental parameters: (1) Partial Least Squares Regression (PLSR), (2) Support Vector Regression (SVR), (3) Artificial Neural Networks (ANN), and (4) Random Forest Regression (RFR). For different model training and implementation approaches, the following strategies were considered: (1) Global Window Strategy (GWS), (2) Conceptual Window Strategy (CWS), (3) Regular Moving Window Strategy (RLWS), (4) Object-Based Window Strategy, and (5) Decision-Level Integration Window Strategy (DIWS). The second approach presents a novel physical model for enhancing the spatial resolution of LST using energy balance equations across different land cover types. For the first time, this model combines the Temperature Separation Principle (TSP) and Thermal Unmixing Model (TUM) frameworks to improve accuracy. This integration ensures that the physical nature of the spatial resolution enhancement process significantly mitigates scaling effects on LST accuracy, maintaining or improving the absolute accuracy of LST. The study uses diverse datasets, including imagery from Landsat 8 and MODIS Terra satellites, land cover maps, impervious surface percentages, digital elevation models, building heights, population density, and ground-based measurements. The study area included six cities in the United States (Chicago, Dallas, Minneapolis, Phoenix, Seattle, and Kansas), 13 cities in Europe (Lisbon, Madrid, Zamora, Bucharest, Vienna, Prague, Paris, London, Warsaw, Copenhagen, Herning, Stockholm, and Helsinki), and one city in Iran (Tehran). The findings reveal that in urban and agricultural areas, biophysical characteristics predominantly influence LST distribution, whereas topographical features have a greater impact in mountainous regions. Urban areas exhibit stronger effects of surface texture and neighborhood characteristics on LST distribution compared to other regions. Incorporating neighborhood effects and landscape parameters in the spatial resolution enhancement process reduced the LST error by 0.8 K in warm seasons and 0.4 K in cold seasons. Furthermore, improving the spatial resolution of LST from 1000 m to 30 m using the regression-based model at the decision-making level and the SEB model reduced the LST error by an average of 2.5 K (3.4 K) in warm seasons and 1.2 K (1.8 K) in cold seasons. The SEB model also provided additional insights into temperature distribution by accounting for evapotranspiration and energy fluxes. These findings underscore the high potential of the proposed approaches in simultaneously improving the spatial resolution and accuracy of LST, making them highly applicable for environmental and urban studies.
Keywords: LST, Spatial Resolution Enhancement, Surface Energy Balance, Regression Models, Decision-Level Integration
How to cite: Karimi Firozjaei, M., Rahimi, M., Kiavarz, M., Rahimi, L., AghaKouchak, A., De Michele, C., and Manfreda, S.: Enhancing Spatial Resolution and Accuracy of Land Surface Temperature: Integration of Regression-based and Surface Energy Balance Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2096, https://doi.org/10.5194/egusphere-egu25-2096, 2025.
Terrestrial evaporation (E) is a critical climate variable that links the water, carbon, and energy cycles. It plays a vital role in regulating precipitation, temperature, and extreme events such as droughts, floods, and heatwaves. In hydrology, E represents a net loss of water resources, while in agriculture, it determines irrigation demands. Despite its significance, global E estimates remain uncertain due to the scarcity of ground-based measurements, the complexity of physiological and atmospheric interactions, and challenges in capturing E through satellite observations. Addressing these limitations, the fourth generation of the Global Land Evaporation Amsterdam Model (GLEAM4¹) enhances the representation of E and its components by improving the representation of key processes such as interception loss, atmospheric water demand, soil moisture dynamics, and plant groundwater access. Using a hybrid framework that combines machine learning for evaporative stress estimation with physical principles, GLEAM4 balances interpretability with adaptability and validation against hundreds of eddy-covariance sites demonstrates its robustness and improved performance.
Building on GLEAM4, efforts are underway to develop a high-resolution (1 km) E dataset tailored to the needs of agriculture, water management, and climate adaptation. GLEAM-HR downscales precipitation from MSWEPv2.8 and radiative forcing data by optimally merging LSA SAF and MODIS. The innovations introduced in GLEAM-HR address fine-scale E dynamics, particularly in agricultural regions, while enabling the characterization of droughts, heatwaves, and water resource distribution in vulnerable areas. Preliminary results from GLEAM-HR over the Meteosat disk (covering Europe and Africa) highlight its potential to tackle water-related challenges, support sustainable water management practices, and contribute to evidence-based decision-making. In the future, the data products will be available publicly through an interactive 3D data cube application.
¹Miralles, D.G., Bonte, O., Koppa, A., Baez-Villanueva, O.M., Tronquo, E., Zhong, F., Beck, H., Hulsman, P., Dorigo, W., Verhoest, N.E. and Haghdoost, S. GLEAM4: global land evaporation dataset at 0.1° resolution from 1980 to near present, 20 November 2024, PREPRINT (Version 1) available at Research Square (https://doi.org/10.21203/rs.3.rs-5488631/v1)
How to cite: Baez-Villanueva, O. M., G. Miralles, D., Bonte, O., Koppa, A., Massant, J., Ruan, F., Söchting, M., and Mahecha, M.: Towards high resolution evaporation data integrating satellite observations and hybrid modelling over Europe and Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16431, https://doi.org/10.5194/egusphere-egu25-16431, 2025.
Quantifying evapotranspiration (ET) beyond the local scale is essential for many water-related studies. Compared to in-situ instruments, Remote sensing (RS) has allowed the continuous monitoring of ET at larger spatial scales. By exploiting the physical relationship between remotely sensed surface biophysical parameters and the Earth’s thermal emission, continuous ET at such spatial scales can be obtained. In this study, we applied EVASPA, a tool that provides an ensemble of ET estimates, among other surface energy balance (SEB) variables, from various sources of data and several algorithms. Here, we applied MODIS data, which included: Land Surface Temperature/Emissivity (LST/E), NDVI, albedo, among others. Landsat data was separately applied for estimates at relatively high spatial resolution. Our multi-data multi-method approach resulted in 1215 ET estimates for the MODIS-based ETs (i.e., 5 LST/E (MYD/MOD 11/21 and VIIRS 21); 3 radiation sources (ERA5Land, MSG, MERRA); 9 Evaporative Fraction methods (5 S-SEBI based, 4 T-VI based), and 9 Ground heat flux methods (based on NDVI and LAI)). Evaluations using in-situ flux data yielded reasonable results even when a simple average was used (for example, RMSE of ~0.9 mm/d over the forested Puechabon site), with a broad absolute and performance range between the member estimates being observed (for instance, an ensemble RMSE range of ~0.6 to ~1.2 mm/d for the best-to-worst performing EVASPA members over the Puechabon site). Uncertainty analyses were also performed where we analysed how each of the distinct variables (i.e. radiation, LST, EF and G methods) influenced the modelled ET. Irrespective of the combination criteria selected, LST and EF were observed to be the main uncertainty drivers; this was despite instances where radiation resulted in higher uncertainties that were dependent on the combination selected and/or the period of simulation. G flux methods exhibited the least influence on the ensemble simulations. Overall, we showed that ensemble-based contextual modelling can provide enough spread for better flux simulations. This work aims to guide the establishment of an optimal weighting criteria of the members for improved ET estimates. The EVASPA algorithms will be used for providing ET estimates in the frame of the Indo/French future mission TRISHNA to be launched by the end of 2026.
Keywords: ET, SEB, contextual ET, multi-method multi-data, ensemble modeling.
How to cite: Mwangi, S., Olioso, A., Boulet, G., Etchanchu, J., Rivalland, V., Farhani, N., Demarty, J., Ollivier, C., Mallick, K., Hu, T., Jia, A., Sarrazin, E., Gamet, P., and Roujean, J.-L.: Ensemble evapotranspiration estimates and uncertainties: EVASPA, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16717, https://doi.org/10.5194/egusphere-egu25-16717, 2025.
Historically, the Netherlands has predominantly managed water surpluses, consequently numerical hydrological models are calibrated and validated under conditions ranging from average to wet. However, as prolonged drought periods become more frequent, there is a growing need for models to simulate dry conditions. One of the key processes in drought simulation is evaporation (E). This study seeks to provide a deeper understanding of the quantification of actual evaporation (Eact) under dry circumstances in the Dutch agricultural sector and for water management practices. For this purpose, an extensive monitoring plan was implemented to estimate actual and potential evaporation (Epot) as well as soil moisture content, on an agricultural site in the Netherlands. A comparison between Epot and Eact during the drying and wetting phase is proposed to conduct an uncertainty analysis on various calculation and measurement methods. Furthermore, we will study the land- atmosphere interactions that influence Eact, and the effect of irrigation.
How to cite: Bos-Burgering, L., Coenders-Gerrits, M., and Uijlenhoet, R.: Quantification of actual evaporation through different in-situ techniques for Dutch water management practices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1261, https://doi.org/10.5194/egusphere-egu25-1261, 2025.
Quantifying and partitioning evapotranspiration (ET) of agricultural ecosystems in various environmental settings enable studying the site-specific determinants of plant water use. The aim of the study was to conduct reliable and reproducible field-scale partitioning of ET into its component fluxes soil evaporation (E) and plant transpiration (T) from water stable isotope analysis (δ2H and δ18O). Isotope-based partitioning methods, because of their methodological independence to other, traditional, experimental or data-driven approaches, are useful for intercomparison. Campaigns were carried out at two agricultural field sites of the ICOS ecosystem thematic network, differing in their hydroclimate and crop settings. Isotopic partitioning was achieved by simulating (i) the isotopic composition of ET (δET) from atmospheric water vapor measurements and (ii) δE and δT from simultaneous destructive sampling of soil water and plant xylem water. Campaigns were carried out from June 22 to July 19, 2022 (sunflower crop, mean air temperature and relative humidity: 24 °C and 65 %) and from March 27 to April 18, 2023 (winter wheat, 12 °C and 75 %) in the Mediterranean site FR-Aur (Auradé, France), and from June 5 to August 26, 2024 (winter wheat, 19 °C and 73 %) in the temperate site DE-RuS (Selhausen, Germany). Up to three measurements per day of isotope-based partitioning results were confronted against estimates of ET obtained from on half-hourly eddy-covariance data. Non-isotopic ET partitioning was calculated based on simulations using half-hourly sap flow- (T) and daily microlysimeter measurements (E) during the 2022 campaign in Auradé. Both the non-isotopic and isotopic data showed an increase in daily T/ET ratios during the 2022 campaign. Daily mean T/ET ratios were 0.79 from sap flow/EC data, 0.75 from sap flow/microlysimeter data, and mean sub-daily isotopic T/ET ratios of 0.66 for sunflowers in 2022. T/ET ratios of winter wheat in Auradé 2023 showed a mean value of 0.92. The differences between the isotopic and non-isotopic T/ET ratios in 2022 might be a result of differences in measurement footprint, as field-scale EC-based partitioning was compared to sub-field scale isotopic partitioning. Estimation of T/ET uncertainty, calculated as from propagation of errors of the individually conducted measurements, was provided. While errors of daily sap flow/EC partitioning were lower compared to microlysimeter/EC and isotopic partitioning, errors of sub-daily EC/sap-flow T/ET exceeded the errors of the other two approaches. In addition, values of sap flow/EC T/ET increased over 100% from the late afternoon, showing a limitation of the sap flow/EC-based partitioning method on the sub-daily timescale. During the 2024 campaign, isotopic measurements were performed at an hourly resolution, and analysis of isotopic and non-isotopic T/ET ratios for the 2024 campaign is pending. The aim of future campaigns is the continuation of intercomparison between partitioning methods and the identification of differences and fit among T/ET partitioning approaches specifically to the considered temporal and spatial scales.
How to cite: Schulz, D., Boulet, G., Brüggemann, N., Brut, A., Le Dantec, V., Tallec, T., and Rothfuss, Y.: Multi-site investigation of the determinants of evapotranspiration partitioning with a mobile water isotope laboratory, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1043, https://doi.org/10.5194/egusphere-egu25-1043, 2025.
The tropics play a pivotal role in the terrestrial energy and water cycles, as well as regulating the carbon cycle. The increasing pressures over the remaining natural vegetation areas in Brazilian tropical forests, allied to climate change, are likely expected to alter these cycles. Despite the existence of studies that have already observed changes on water and energy fluxes, questions regarding heat and mass exchange mechanisms and the biophysical processes in tropical ecosystems and crops for food and energy production remain. In order to enhance the knowledge towards these research questions, in-situ monitoring with high spatial and temporal resolutions are needed. This project aims to use and validate advanced approaches used to monitor and model water vapor, energy, and greenhouse gases (GHG) fluxes through in situ monitoring (sampling) in strategic land covers and forest ecosystems. With this purpose, besides a fixed-continuous monitoring in a wooded Cerrado (a tropical woodland) equipped with an Eddy Covariance system, a mobile set up monitoring system to water and energy fluxes, and GHG concentrations measurements will be used in different areas. This system will be a non-steady-state flux chamber connected to a closed-path gas analyzer. The target monitoring areas include different land covers (soybean, pasture, sugar cane, and other agricultural areas) and undisturbed areas (wooded Cerrado and riparian vegetation). The expected outcomes will contribute to improve methodologies and models through the better comprehension of the dynamics and the shifts of the water, energy and GHG fluxes. After the in-situ monitoring following a representative sampling criteria to catch both seasonal and spatial variabilities to measure the selected fluxes, mathematical models will be calibrated to allow the expansion of the timeseries and simulations including possible variations in the input variables. Afterwards, the observations, parameters, and simulations will serve as input for hydrological repositories, carbon inventories, and new contributions about water, energy, and carbon fluxes in a tropical region. Disclaimer: This abstract describes an ongoing project. Please note that it does not contain any results or conclusions, as the work is still in progress.
How to cite: Ayach Anache, J. A., Wendland, E., Jardim Machado, L., de Sousa Pantarotto, H., and Almeida Dutra Júnior, S.: Progress in implementing diverse strategies to enhance the understanding of water vapor and greenhouse gas fluxes across varied land covers in tropical regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7715, https://doi.org/10.5194/egusphere-egu25-7715, 2025.
Drone surveys were conducted at a cropland at Kartal, Hungary in 2024 to estimate the evapotranspiration (ET) of the area. There is an eddy covariance tower in the cropland since 2017. Between 27 May and 8 August 9 campaigns were carried out with a DJI M300 drone equipped by a Micasense Altum (MA) multispectral and thermal camera. The leaf area index (LAI) was also measured at 7 points in the sunflower canopy supplemented light interception measurements to estimate the leaf angle distribution of the canopy. Canopy cover, surface temperature, and LAI maps were produced from the MA’s reflectance values and the LAI samples in the 7 points using partial least squares (PLSR) regression to serve as inputs of the pyTSEB model. The spatial average of the ET pixels from the footprint area of the corresponding eddy covariance flux were validated against the eddy covariance ET.
The first results of validation showed very weak relation between the measured and modelled data. The relationship improved considerably when the surface temperature maps taken by the MA were corrected according to the surface temperature measured from the eddy tower by an Apogee infrared radiometer.
Further improvement was reached when the LAI maps were modified based on the leaf angle distribution estimated from the light interception measurements.
While the correlation between the measured and modelled ET is statistically significant, the intercept of the regression is a considerable (~100 W m-2).
How to cite: Pintér, K. and Nagy, Z.: Uncertainties of drone-based cropland evapotranspiration estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18907, https://doi.org/10.5194/egusphere-egu25-18907, 2025.
Evapotranspiration (ET) is the primary pathway for dissipating terrestrial water resources, and a key process in regulating surface temperature. The Ts-VI feature space method is an important evapotranspiration simulation approach, reflecting the relationship between vegetation cover and the temperature-evapotranspiration response, while effectively balancing model complexity and efficiency. The key issue for Ts-VI feature space methods lies in the accurate identification of the four extreme endmember temperatures. However, the differences in feature points caused by applying the theoretical trapezoid framework at the pixel or areal scale have received little attention. The discrepancies and uncertainties between these two approaches, along with the resulting contradictions in trapezoid framework and differences in evapotranspiration simulation, are often neglected. This study firstly develops a fully explicit theoretical method for determining extreme endmember temperatures, simplifying the process and improving computational efficiency. Secondly, using the above explicit equation, a systematic comparison is conducted across four single-source Priestley-Taylor-based evapotranspiration models using four methods for determining extreme endmember temperatures: the empirical fitting method (EFM) as a reference, envelope theoretical method (ETM) and pixel theoretical method (PTM) at the areal scale, and the same pixel theoretical method with flux site observational meteorological data (PTMs). Thirdly, we analyzed the spatiotemporal variations of extreme endmember temperatures and their positional relationships within the trapezoidal framework across these different methods, and discussed their uncertainties through envelope analysis and sensitivity analysis. Using all site-year data from 9 AmeriFlux sites in the Southern Great Plains, along with MODIS and NCEP products from 2017. The results show that the proposed explicit theoretical calculation method is effective, with the four methods demonstrating the best validation results when compared to observed flux data, closed using the residual method, yielding RMSE values of 1.70 mm/d, 1.55 mm/d, 1.53 mm/d, and 1.51 mm/d, respectively. During the growing season of 2017, ETM exhibited an exceptionally high peak at the dry edge, while PTM and PTMs displayed frequent and dense high-value spikes, with particularly pronounced intensity. The positional discrepancies among the different trapezoidal frameworks were primarily observed at the dry edge, with PTM and PTMs showing a higher probability of the highest dry edge. Envelope analysis revealed that ETM, PTM, and PTMs occasionally failed to envelope all Fc-LST scatter points, leading to overestimations of evapotranspiration, particularly at the wet edge. In summary, this study provides a comprehensive understanding of the theoretical trapezoidal framework, highlighting the discrepancies and uncertainties across different scales, and offers valuable insights for model implementation and improvement.
How to cite: Yang, L., Guan, H., and Shang, S.: Discrepancies and Uncertainties in the Application of the Fc-LST Theoretical Trapezoid Framework at Pixel and Areal Scales Using a Priestley-Taylor based Evapotranspiration Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2073, https://doi.org/10.5194/egusphere-egu25-2073, 2025.
Colorado River water has been allocated through recent Minutes (319 from 2014-2017; 323 from 2018-2026) to the 1944 Water Treaty between the United States and Mexico to support efforts to restore native riparian forests, which provide essential habitat for migratory birds, in the Colorado River delta. Our study was largely conducted in the context of assessing the effects of restoration efforts on riparian corridor health. We processed and analyzed remotely sensed data from 2000 to 2023 to assess large-scale dynamics of vegetation health by measuring satellite vegetation index (VI, a proxy for canopy greenness) and plant water use (actual evapotranspiration, ETa) in the riparian corridor.
Under Minute 323, water deliveries are used primarily to irrigate managed restoration areas. Our study reports the outcomes of restoration actions on variables such as vegetation extent and density through two-band Enhanced Vegetation Index (EVI2) measurements and hydrological processes including ETa. We integrated EVI2 with potential ET from two sources, the Yuma Valley Arizona Meteorological Station “AZMET” ground station and gridded Daymet, to calculate ETa. We quantify ETa in restoration sites compared to the unrestored reaches from 2000-2023. Our findings showed an average increase of 42% in EVI2, an indication of land cover greenness, within the restoration sites in the decade since 2014, when efforts by many non-government organizations collaborated to improve the riparian corridors, with one large effort in Reach 2 and a dozen smaller sites in Reach 4. Conversely, greenness in adjacent, unrestored areas in these reaches declined by 27%. The study also indicates a 22% increase in ETa in the restored areas, compared to a 31% reduction in the unrestored regions. Restored sites in Reach 4, which contains a dozen restoration areas, experienced ETa increases ranging from 9-12%, whereas their unrestored counterparts show a decline of 21%. Restoration efforts focusing on small plots have successfully revitalized habitat, the motivation for this research.
Measurements of VIs and ETa several years after the Minute 323 federal flows were delivered in 2020 and 2021 to the riparian corridor, including to restoration sites in Reaches 2 and 4, do not show any boost to the greenness and ETa in the unrestored riparian reaches in the delta after these federal flows were delivered. However, further downstream, in Reaches 5 and 7, the non-native shrub saltcedar (Tamarisk spp.) has been repeatedly defoliated by saltcedar beetles (Diorhabda spp.). Select regions of these defoliated shrubs in Reaches 5 and 7 were measured using Landsat time series data from 2000-2023 using peak growing season dates of May 1 through October 30. The measured change between the ETa in the first five years (2000-2004), with a mean of 737 mm/year, and latter five years (2019-2023), with a mean of 599 mm/year, showed a decrease of 138 mm/year in ETa, which is a decrease in ETa of 18.7%. Despite the challenges posed by small water deliveries and beetle defoliation for non-native saltcedar shrubs, restoration efforts focusing on small plots have successfully revitalized habitat, the motivation for this research.
How to cite: Nagler, P., Wildermuth, L., Shafroth, P., Gonzalez-Sargas, E., Gomez-Sapiens, M., Jimenez-Hernandez, E., Barreto-Muñoz, A., and Didan, K.: Restoration Efforts in Riparian Ecosystems in the Colorado River Delta as Measured by Greenness Indices and Evapotranspiration (ET) and using Hydrology, Avian Studies and ET Change Maps , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1731, https://doi.org/10.5194/egusphere-egu25-1731, 2025.
Previous datasets have limitations in generalizing evapotranspiration (ET) across various land cover types due to the scarcity and spatial heterogeneity of observations, along with the incomplete understanding of underlying physical mechanisms as a deeper contributing factor. To fill in these gaps, here we developed a global Highly Generalized Land (HG-Land) ET dataset at 0.5° spatial resolution with monthly values covering the satellite era (1982–2018). Our approach leverages the power of a Deep Forest machine-learning algorithm, which ensures good generalizability and mitigates overfitting by minimizing hyper-parameterization. Model explanations are further provided to enhance model transparency and gain new insights into the ET process. Validation conducted at both the site and basin scales attests to the dataset’s satisfactory accuracy, with a pronounced emphasis on the Northern Hemisphere. Furthermore, we find that the primary driver of ET predictions varies across different climatic regions. Overall, the HG-Land ET, underpinned by the interpretability of the machine-learning model, emerges as a validated and generalized resource catering to scientific research and various applications.
How to cite: Feng, Q., Wang, D., and Zeng, Z.: Long-term gridded land evapotranspiration reconstruction using Deep Forest with high generalizability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2848, https://doi.org/10.5194/egusphere-egu25-2848, 2025.
Groundwater evapotranspiration (ETg) is a crucial upward water flux in the water budget, especially in arid and semi-arid saline areas. Modeling ETg is challenging as it involves complex biogeophysical processes in both soil and vegetation dynamics. However, these processes are vastly oversimplified in commonly used process-based models like MODFLOW, where the ETg modeling relies solely on groundwater table depth. To disentangle this issue, this study presents the Evapotranspiration Package with Multi-factor (ETM), an enhancement to MODFLOW which additionally incorporates soil properties, vegetation information, and salinity levels to simulate spatiotemporal ETg. Compared to the original MODFLOW-EVT package, the proposed ETM package mitigates structural uncertainty by involving external soil and vegetation information based on optical remote sensing data. We conducted intensive experiments in Hetao, a one-thousand-year irrigation district in China. Daily groundwater table depth time-series for 108 observation wells were collected and used for calculating ground truth ETg based on the groundwater level fluctuation method. We evaluate the proposed ETM package in both well-level and regional-level experiments. In the well-level experiments, the ETM outperformed the EVT package with the coefficient of determination increasing from -1.698 to 0.449 and the RMSE reducing from 1.906 mm to 0.861 mm. Additionally, we employed the ETM package to model regional ETg for a 3,000-ha experimental area. Compared to the original EVT package which primarily considers groundwater level and results in more homogeneous outputs, the proposed ETM package demonstrated diverse ETg estimates in which the spatial pattern aligns with the prior knowledge. This improved approach addresses the shortcomings of previous models and contributes to more informed agricultural water resource management and planning through a deeper understanding of groundwater dynamics.
How to cite: Zhao, L., Wu, J., Yang, Q., and Gobin, A.: Improving groundwater evapotranspiration modeling in saline areas by integrating remote sensing data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5188, https://doi.org/10.5194/egusphere-egu25-5188, 2025.
Partitioning evapotranspiration (ET) into soil evaporation (E) and plant transpiration (T) is crucial for accurate water resource management. Traditionally, this has been challenging due to the complexity of the underlying processes. In this study, we develop an approach to enhance the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) model, enabling better partitioning of landscape-scale flux components. Named METRIC-2S, this approach introduces a two-source scheme into the original single-source model, using soil and vegetation temperatures for partitioning. These temperatures are used by METRIC to calculate two ET components, one for soil and another for vegetation, subsequently weighted by fractional vegetation cover (fc) to compute E and T. Soil and vegetation temperatures are estimated using the hourglass method, driven by surface temperature and fc. ET estimates from both the original METRIC and the revised METRIC-2S models are compared and validated against eddy covariance measurements over three agricultural sites: an olive orchard, a wheat field, and a mixed wheat/olive plantation. METRIC-2S demonstrates significant improvements in accuracy relative to the original METRIC model across all three sites, with reductions in RMSE from 141 to 63 W/m2 at the olive site, 102 to 83 W/m2 at the wheat field, and 180 to 78 W/m2 at the mixed site. To evaluate the performance of the partitioning scheme, transpiration estimates were compared with available sap flow measurements at the olive orchard site on selected dates coinciding with a Landsat overpass, yielding an RMSE of approximately 22.3 W/m2. While further verification and assessment of component values are necessary, the results suggest that the METRIC-2S approach strikes a good balance between simplicity and improved accuracy.
How to cite: ElFarkh, J., Ait Hssaine, B., and Chehbouni, A.: METRIC-2S: A Two-Source Model for Enhanced Partitioning of Evapotranspiration in Agricultural Landscapes , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19251, https://doi.org/10.5194/egusphere-egu25-19251, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot A
EGU25-12878 | Posters virtual | VPS10
Remote-Sensing based Global Evapotranspiration estimates at high spatial resolution through Sentinel-2 satellite imagery and meteorological data. The CWRweb tool.Thu, 01 May, 14:00–15:45 (CEST) | vPA.17
This work introduces a new webGIS tool to estimate the Crop Water Requirement (CWR), using time series of satellite images and meteorological data, at high spatial resolution and a global scale. This CWRweb tool provides users with information on the temporal evolution of the CWR, as a first approach of the crop evapotranspiration, as well as other parameters of interest. This process is implemented via web and requires no proficiency in remote sensing.
The implemented calculation of the evapotranspiration under standard conditions (ETc) stands on the robust FAO-56 methodology, based on the relationship between the Crop Coefficient and the Reference Evapotranspiration (Kc-ETo). The CWRweb tool adopts the single crop coefficient approach, combining the effects of both, crop transpiration and soil evaporation into a single coefficient (Kc). These Kc values derive from the NDVI time series of Sentinel-2 multispectral satellite images, for a broad range of crops (horticulture, woody crops, and other crops) and natural vegetation, assuming a general component for the soil evaporation.
The CWRweb tool benefits from the potential of the Sentinel-2A & B satellite constellation to provide users with free time series of images with a spatial resolution of 10m × 10m and a revisit frequency of 2-3 days. The high frequency of Sentinel-2 imagery allows to obtain daily Kc values through interpolation of NDVI data from cloud-free images at high spatial resolution. Online access to massive databases of satellite images, such as those of the Copernicus Data Space Ecosystem program (https://dataspace.copernicus.eu/), together with recent advances on meteorological numerical models to provide global ETo layers at different gridding size, are boosting the operational use of the CWRweb tool.
The CWRweb tool runs and graphically displays daily ETc, as well as NDVI, Kc, and ETo values used in its calculation, for a selected time interval. Results can be provided at both, field and pixel scales. An assessment of the CWRtool was conducted by comparison against the OpenET tool on a selection of crops-sites in California, USA. An average uncertainty of RMSE=0.9 mm·d-1, with a negligible bias, was obtained in a performance analysis using the OpenET ensemble outputs as a reference, using 15 different locations, and data for the period 2016-2024. These results are promising and reinforce the potential of the CWRweb tool for the operational estimation of global evapotranspiration at a high spatial resolution.
How to cite: Campoy, J., Sánchez, J. M., Beltrán, A., Pérez, Y., Molina, A., and Calera, A.: Remote-Sensing based Global Evapotranspiration estimates at high spatial resolution through Sentinel-2 satellite imagery and meteorological data. The CWRweb tool., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12878, https://doi.org/10.5194/egusphere-egu25-12878, 2025.
